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Record W7037727786

Evolution of an international research collaborative in HIV and rehabilitation:Community engaged process, lessons learned, and recommendations

2018· article· en· W7037727786 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Context (archaeology)RehabilitationSustainabilityCapacity buildingKnowledge transferHealth care
DOInot available

Abstract

fetched live from OpenAlex

Background: Human immunodeficiency virus (HIV) is increasingly considered a chronic illness. Rehabilitation can address some of the health challenges of people living with HIV (PLWHIV); however, the field is emerging. Objectives: We describe our experience establishing an international collaborative in HIV and rehabilitation research using a community engaged approach. Methods: The Canada-UK (now Canada-International) HIV and Rehabilitation Research Collaborative (CIHRRC) is a network of more than 85 PLWHIV, researchers, clinicians, and representatives from community-based organizations collectively working to advance knowledge on HIV and rehabilitation. Results: Activities and outcomes include facilitating knowledge transfer and exchange (KTE), establishing and strengthening multistakeholder partnerships, and identifying new and emerging priorities in the field. Collaboration and support from community organizations fostered mechanisms to raise the profile of, and evidence for, rehabilitation in the context of HIV. Considerations of scope, partnership, and sustainability are important. We offer recommendations for developing an international community–academic–clinical research collaborative using a community-engaged approach. Conclusions: Research networks involving community– academic–clinical partnerships can help to promote KTE and establish a coordinated response for addressing priorities in an emerging field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.427
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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